The Regressive Demands of Demand-Driven Development

There is a frustratingly weak and positive finding in the literature that examines the targeting performance of social funds projects, which, over time, took on many of the characteristics of community-driven development programs and became an important part of the social protection strategy in many countries by funding projects that provide public (and sometimes private) goods requested by communities: they are only moderately pro-poor. This is despite the fact that many well-meaning practitioners are trying their best to target funds to poor areas and poor households within those areas, often using the best data available on the geographic distribution of poverty.

The reasons given in the literature as to why the final incidence of benefits is not strongly pro-poor have usually had something to do with elite capture: differences in preferences between village elites and others; local inequality and elite capture; elites steering benefits towards their kinship and client networks; use of such funds for political gain; and corruption. However, given their demand-driven nature, the beneficiaries of these programs are determined by two equally important processes: (i) who applies; and (ii) who, having applied, gets approved. It seems eminently plausible that at least part of the reason why poor people don’t benefit more from these programs is that they may not be applying for funding in the first place. The extant literature has examined the determinants of being a program beneficiary (i.e. the final funding or project allocations) in great detail, but I know of no studies that decompose targeting performance into applications and, conditional on application, selection.

A recent paper by Baird et al. (2011) does exactly this. Using an unusually rich data set that combines the universe of applications and funding decisions on the second phase of Tanzania’s Social Action Funds (TASAF II) with poverty maps, census data, and voting data for each of the more than 2,000 wards in mainland Tanzania, they find the following:

·The application process is regressive with project applications substantially more likely to come from richer districts. This pattern is strongly correlated with variation in access to media and information and with political participation across districts, such as voter registration and turnout.

·The subsequent project approval process is pro-poor, due largely to the pre-determined funding allocations made from the center to the districts.

·The progressive selection of projects from an initially skewed pool of applications reproduces the familiar finding of moderately pro-poor final funding allocations in the extant literature.

In addition, the authors have data for every household in 100 villages in five districts. These data were collected to assess the impact of a component of TASAF II, under which groups of vulnerable individuals get large grants from TASAF to carry our income-generating activities (such as animal husbandry, milling, tailoring, etc.) they proposed. As such, these are projects that provide private goods to a small percentage of individuals in these villages (please refer to the paper on details of how this component of TASAF II works). The findings at the local level are remarkably similar to those found at the national level:

·Among households eligible for this component of TASAF II (according to the exogenous vulnerability criteria imposed by the center), less than 50% had ever heard of the program: in fact, ineligible households were significantly more likely to have heard of TASAF. Awareness of TASAF II (which seems necessary to apply) is positively correlated with education, ownership of a radio or phone, attending village meetings, and being related to village elites.

·Among those who heard of TASAF II, eventual program beneficiaries are poorer, but also still more likely to be politically active and live close to the village center. Again, as at the national level, the targeting criteria imposed by the center is responsible for the fact that the program is pro-poor: there is little evidence of pro-poor selection of beneficiaries within villages conditional on eligibility.

So, there is a decent amount of evidence here suggesting that the requirement that a household or a community has to submit an application in order to become a beneficiary, may in fact be hindering the ability of such programs to reach poor areas and poor households within those areas. The lack of applications from poor households and communities could be due to various factors, among which lack of information, proposed in this paper, is but one. For example, it is possible that poor households (or communities) are aware of the program but are unable to navigate the system to produce valid applications. The paper presents some evidence at the household level suggesting that this may be part of the story: group leaders (i.e. the chairperson, secretary, and treasurer, who hold signatory power over the group accounts) for the proposed income generating activities are substantially more educated, more likely to own phones and radios, and less likely to be poor than the “rank and file” members of these groups. It is likely that having a small number of such individuals among the beneficiaries is instrumental in putting together viable project proposals and navigating the application process.

Another possibility is that households and communities that are aware of TASAF II (and able to apply) nonetheless decide against doing so because of the costs associated with the projects. Many programs, including TASAF II, require that communities contribute a share of the project costs. While this is a reasonable hypothesis, the authors do not find support for it in the data: application patterns are the same for projects that require cost-sharing (infrastructure and public works) and those that do not (vulnerable groups).

Finally, low application rates among the poor might arise if they rationally decide not to apply due to a perceived low probability of being approved. The authors do not find support for this hypothesis, either. As can be seen in the Figure below, approval rates are higher in poorer wards, meaning that, if anything, application rates from these areas should be higher.

Finally, a few thoughts on what these findings might imply for policymakers in charge of designing programs:

·When possible, geographically disaggregated poverty maps should be used to target initial funds to smaller administrative units – if the funds are aimed to reach the poor.

·The initial propagation of information about the program (sensitization and outreach), and setting some simple eligibility criteria appear to be particularly important activities over which to consider maintaining some centralized control. Of course, this is subject to the appropriate caveats depending on the broader goals of social funds programs in a decentralized setting.

·The study confirms findings in the literature with respect to the advantages enjoyed by local elites in decentralized programs, while suggesting a new culprit for this pattern. Despite the fact that community development projects are supposed to be designed to address the needs of the “poor, the marginalized, and the excluded”, these are exactly the groups among whom the awareness of the program is lowest. Furthermore, the importance of civic engagement and political connections permeates the findings: unlike measures of poverty, variables measuring political activity and connectedness increase both the demand-side probability to seek out the program as well as the supply-side probability of selection. While the authors cannot distinguish active ‘informational capture’ by elites from the fact that marginalized groups are simply harder to reach and inform, the informational regressivity that pervades this study motivates a strong focus on outreach efforts in CDD programs.

Community development programs require their potential beneficiaries to be aware of and fully participate in the entire process, but the ability to do so is not equitably distributed across the population. Rather, it is significantly lower among the poor, the vulnerable, and the marginalized. Inducing meaningful participation at the local levels remains the big hurdle for these programs to truly succeed.

Comments

Dear Berk - thanks for a nice blog posting on an issue which I too have recently encountered with a similar income generating component in the Morocco National Human Development Initiative (INDH). There too, the design of the component was basically structured as a competitive application (much like our internal TF calls for proposals) with criteria for selection that of course favor pro-poor applications say from community associations. What one found there from a rapid review was a different problem - there the selected grantees were actually representative of the poor because in most cases the project facilitation teams went out and identified them first and encouraged them to apply. However, you saw a different problem, namely that almost all the activity ideas they came up with were commercial non-starters. To me, this reflected an inherent conflict between two objectives of component - (a) to target the poor, and (b) to come up with viable income generating activities. Usually the two don't go together - and my sense is that TASAF-2 is facing a similar issue.
What I see therefore is a trade-off between targeting and running a demand-driven competition and CDD/social fund programs are always going to face this. The solution in places like India and Brazil where the livelihood side of CDD has been far more successful than other places is to seperate out the 'competitive funding' part of the program to really only target the top performers (most likely non-poor) and then have a targeted TA program for building capacity of poor HH or CBOs so that they can benefit from the income generating activity focus. Perhaps TASAF-3 needs to go down that route as well, especially since I believe the program will now be completely oriented around private goods (cash transfers, and livelihoods support).
Cheers,
Janmejay

Dear Janmejay,
You have perfectly captured the tension in these programs between targting the poorest, most vulnerable individuals and then asking those people to run successful small enterprises -- we point this out in the concluding section of the paper. In fact, when I was first asked to evaluate this component of TASAF II five years ago, my collaborators and I were quite skeptical that this could ever work. I am sure that your suggestions are among those being discussed by the team of people who are in charge of designing TASAF III.
Nonetheless, with the cooperation of good TTLs and a great team from TASAF, we did set up a rigorous impact evaluation (using randomized delay, like PROGRESA) of these projects. And, while a preliminary look at the short-term results (one year after disbursement of project funds) does not give us a definitive answer of success or failure, we are seeing some asset accumulation (particularly livestock) at the HH level, even among the "rank and file" members, i.e. those with the least amount of education, acess to information, and consumption. We're currently in the field collecting data to assess two-year impacts.
A few things that happened during the TASAF II process may have been helpful in producing viable project proposals for IGAs. First, TASAF had a lot of checks in place to make sure that the proposals made sense and had a chance to succeed. Each proposal was vetted by a sector expert, who might reject some at a very early stage. Those who passed this stage worked with facilitators to develop their work plans, budgets, etc. Many of these groups also organically included group leaders, who, while technically eligible, are richer, more educated, more connected, and perhaps more experienced in conducting the proposed activity than the average eligible individual. Finally, as part of the evaluation, we raised funds to provide some business skills training and trust building exercises in a randomly selected sub-sample of treated groups (while beneficiaries appreciated this, they expressed a need for more training -- particularly in extension work). We have some preliminary (and, at this point, tentative) evidence that even this small amount of training may have made a difference in helping the "rank and file" members be more involved in the group activities and accumulate assets during the first year after disbursement.
More analysis and longer-term data will hopefully help shed more light on the question of whether grants for these small income generating activities end up helping the beneficiary households, particularly those most vulnerable "rank and file" members to accumulate assets and escape poverty.

Berk,
your comment, " the tension in these programs between targting the poorest, most vulnerable individuals and then asking those people to run successful small enterprises", would seem to have just as much relevance to the microfinance movement as it does to CDD.
I continue to be amazed at how this relatively intuitive insight continues to be ignored or brushed under the rug by the marketing of the microfinance movement.
Even within the more thoughtful wing of the community the question of "why aren't microenterprises growing?" is too infrequently met with a direct answer of: "because the people running them are among the least educated, least skilled people--and we targeted those people to run businesses on purpose". [For the record I recognize that there are many more answers to the question that are in the process of being answered. Tell David to hurry up and start posting some numbers from the Sri Lanka study)
Tim

Hi Tim,
I agree with the overall premise of your comment, although I am much less familiar with the microfinance literature than then CDD one. However, I should point out that whenever I talk to people who are contributors to that literature, they tell me that our target population in Tanzania is significantly different than microfinance clients in two important ways. First, microfinance lenders do not come near our villages in Tanzania, most likely because it would not (at least not yet) be a sustainable business. Thye situate themselves at urban and peri-urban areas, where there are more people looking to borrow, and sending loan agents around on a regular and frequent basis is relatively cheap. Second, people who seek microfinance loans are likely a self-selected group of individuals who are more likely the entrepreneurial type than our target population of vulnerable individuals who are receiving a grant they don't have to pay back. Hence, while the same argument may apply to poor and uneducated microfinance clients, most would argue that they have a better chance at successfully growing their small entreprise than the TASAF beneficiaries in Tanzania.
However, as I said in my response to an earlier comment, the fact that these activities are not complete failures after one year and are causing some growth of assets at the HH level, is giving me hope that even poor, uneducated, and inexperienced individuals may be able to pull of starting small enterprises given the right combination of investment in human and physical capital. We look forward to analyzing our longer-term impact data to shed more light on these questions.
Berk.

Very interesting article, indeed.
We find a similar case when women end up excluded from programme benefits not as a result of a conspiracy but because the dissemination strategies and the selection process is either ignorant about the channels to reach women or/and biased against women.
Additionally, women are considered a problematic target group in the sense that engaging them as beneficiaries entails another set of activities, in other words more work and strategic planning, such as dealing with domestic violence generated by the implications of women having access to resources or income generating activities in the household or community power distribution, dealing with social norms preventing women from succeeding or even participating, or lack of infrastructures to free women from care activities so that they can take part in programme activities.
It would have been interesting to know how the sex distribution is in the case-studies mentioned in the article both in for the poor beneficiaries and for the actual, relatively speaking, not-so-poor beneficiaries.

Hi Priya,
Thanks for the comments. This is indeed a salient question towards answering which, we designed our evaluation.Group compositions differ significantly by gender, with some groups being predominantly female, some mixed, and some predominantly male. While group formation is endogenous, we intend to examine the gender dimensions of the impacts explicitly in future analysis of impacts, which is just beginning. Will keep you posted.
Berk.

I received the following comments from a reader via email, who consented to me posting them here as comments. The evidence from Mexico mentioned in the work cited is quite relevant to the post, which we had missed:
"Your recent blog on the regressive nature of self-selection was brought to my attention.
David Coady and Susan Parker have a paper on this topic in EDCC (2009; http://ideas.repec.org/a/ucp/ecdecc/v57y2009i3p559-587.html) in the context of Oportunidades. The targeting approach for this program used a combination of self-selection of households at the application stage and administrative selection at the final eligibility stage. They show that self-selection excluded a large proportion of “poor” households mainly reflecting “lack of knowledge”, which in turn reflects the fact that the information/advertizing campaign was more intensive in the poorest urban blocks; most of the poor excluded at this stage were from “non-poor” blocks. Only 78 percent of the poor report knowing about the program and of the 53 percent of the poor excluded, 22 percentage points (i.e. 41 percent) were excluded at the knowledge acquisition stage (see Table 3).
This highlights the importance of program design when introducing a strong element of self-selection into program targeting. But note that their simulations of universal knowledge still result in substantial undercoverage with only 52 percent included in the program (table 3, lower panel, second line, final column). This shows that it is no use solving only one component of the problem; even when 97 percent of the poor apply many are excluded by the administrative proxy-means test due to weak correlation of the index with welfare as measured by consumption per capita. Ultimately, the targeting will be only as good as the administrative selection criteria.
A key message from the paper is the need to view program participation outcomes as a result of a process; one which is sequential (knowledge acquisition, application, and eligibility) but also interdependent (i.e. whether someone decides to find out more about the program and apply will depend on the expected benefits if deemed eligible; this in turn depends on benefits but also the probability of being deemed eligible at later stages of the process). Coady, Martinelli, and Parker develop a model to try to capture this process more formally in a separate paper, an old version of which can be found here: http://ideas.repec.org/p/cie/wpaper/0806.html. Their empirical analysis shows that decisions at each stage of the process are responsive to expectations about the decisions and outcomes at subsequent stages. Note also that this has implications for how they estimate relationships, starting at the end and working backwards.
More generally, in the context of social funds one could argue sensibly that there is an efficiency reason for not targeting the poorest locations. For example, if the quality of projects is expected to be positively correlated with location incomes (e.g. reflecting higher human capital but also maybe the link between population density, average incomes, and returns to investment) then this introduces an efficiency-equity trade-off. In this case, focusing only on “income targeting” may be a bit misleading for policy purposes. This is presumably less so for programs such as Progresa/Oportunidades."